Network screening of Goto-Kakizaki rat liver microarray data during diabetic progression.
Network screening of Goto-Kakizaki rat liver microarray data during diabetic progression.
复制标题
糖尿病进展期间 Goto-Kakizaki 大鼠肝脏微阵列数据的网络筛选
DOI：
10.1186/1752-0509-5-s1-s16
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发表时间：
2011-06-20
影响因子：
--
通讯作者：
Chen L
中科院分区：
文献类型：
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作者：
Zhou H;Saito S;Piao G;Liu ZP;Wang J;Horimoto K;Chen L
2型糖尿病（T2DM）是一种复杂的全身性疾病，伴有明显的代谢紊乱。肝脏是能量代谢的中枢器官，在糖尿病的发展中起着至关重要的作用。虽然自1996年以来，基因表达水平能够通过微阵列测量，但很难评估一种基因表达改变对特定疾病的贡献。其中一个原因是，糖尿病特定阶段的整个网络图缺失，而单个基因必须放入网络图中以评估其重要性。为了确定肝脏中与糖尿病相关的重要转录调控网络，我们在4周(w)、8-12 w和18-20 w的Goto-Kakizaki （GK）大鼠肝脏微阵列数据中通过网络筛选进行了全面的主动调控网络调查。我们在以下程序中通过网络筛选确定GK大鼠的活跃调节网络。首先，利用已知的转录因子与其调控基因的二元关系和生物学分类方案构建调控网络；其次，估计各调控网络与GK大鼠微阵列测量数据的一致性，检测相应条件下的活性网络。在GK大鼠非胰岛素依赖型糖尿病的情况下，通过网络筛选方法对网络与测量数据的一致性进行综合调查，发现：1。糖尿病中中期有更多的通路活跃；2. 炎症、缺氧、凋亡增加、增殖减少和代谢改变是GK菌株的特征，早在4周就表现出来；3. 糖尿病的进展伴随着损伤和代偿；4. 核受体协同工作维持正常的血糖稳健性系统。值得注意的是，这是第一个基于肝脏高通量数据的GK大鼠非胰岛素依赖型糖尿病的综合网络筛选研究。一些重要的途径已被揭示在糖尿病的进展中发挥关键作用。我们的研究结果还表明，网络筛选能够帮助我们理解糖尿病等复杂疾病，并展示了网络系统生物学方法在阐明基本机制方面的力量，这些机制将逃避传统的基于单基因的分析。
Type 2 diabetes mellitus (T2DM) is a complex systemic disease, with significant disorders of metabolism. The liver, a central energy metabolic organ, plays a critical role in the development of diabetes. Although gene expression levels are able to be measured via microarray since 1996, it is difficult to evaluate the contributions of one altered gene expression to a specific disease. One of the reasons is that a whole network picture responsible for a specific phase of diabetes is missing, while a single gene has to be put into a network picture to evaluate its importance. In the aim of identifying significant transcriptional regulatory networks in the liver contributing to diabetes, we have performed comprehensive active regulatory network survey by network screening in 4 weeks (w), 8-12 w, and 18-20 w Goto-Kakizaki (GK) rat liver microarray data. We identify active regulatory networks in GK rat by network screening in the following procedure. First, the regulatory networks are compiled by using the known binary relationships between the transcriptional factors and their regulated genes and the biological classification scheme, and second, the consistency of each regulatory network with the microarray data measured in GK rat is estimated to detect the active networks under the corresponding conditions. The comprehensive survey of the consistency between the networks and the measured data by the network screening approach in the case of non-insulin dependent diabetes in the GK rat reveals: 1. More pathways are active during inter-middle stage diabetes; 2. Inflammation, hypoxia, increased apoptosis, decreased proliferation, and altered metabolism are characteristics and display as early as 4weeks in GK strain; 3. Diabetes progression accompanies insults and compensations; 4. Nuclear receptors work in concert to maintain normal glycemic robustness system. Notably this is the first comprehensive network screening study of non-insulin dependent diabetes in the GK rat based on high throughput data of the liver. Several important pathways have been revealed playing critical roles in the diabetes progression. Our findings also implicate that network screening is able to help us understand complex disease such as diabetes, and demonstrate the power of network systems biology approach to elucidate the essential mechanisms which would escape conventional single gene-based analysis.